clasificacion_binaria_regresion_logistica_postgresql.ipynb

{
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        "<a href=\"https://colab.research.google.com/github/rubuntu/uaa-417-sistemas-de-gestion-de-bases-de-datos-avanzados/blob/main/clasificacion_binaria_regresion_logistica_postgresql.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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      "source": [
        "# Regresión Logística con PosgreSQL\n",
        "\n",
        "La **regresión logística** es un algoritmo de aprendizaje supervisado ampliamente utilizado para problemas de clasificación binaria. Su objetivo es modelar la relación entre un conjunto de variables independientes (características) y una variable dependiente binaria (clase objetivo) que toma valores como 0 y 1. Este algoritmo se basa en el concepto de probabilidad y utiliza una función sigmoidal para transformar valores de entrada lineales en probabilidades comprendidas entre 0 y 1.\n",
        "\n",
        "---\n",
        "\n",
        "### **Fundamentos del Algoritmo**\n",
        "\n",
        "1. **Modelo Lineal:**\n",
        "   En la regresión logística, primero se calcula una combinación lineal de las variables independientes:\n",
        "   $$\n",
        "   z = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + \\dots + \\beta_n x_n\n",
        "   $$\n",
        "   Donde:\n",
        "   - $ \\beta_0 $ es el intercepto.\n",
        "   - $ \\beta_1, \\beta_2, \\dots, \\beta_n $ son los coeficientes de las variables independientes.\n",
        "   - $ x_1, x_2, \\dots, x_n $ son las características de entrada.\n",
        "\n",
        "2. **Función Sigmoide:**\n",
        "   Se aplica la función logística o sigmoide para transformar el valor $ z $ en una probabilidad:\n",
        "   $$\n",
        "   P(Y=1 | X) = \\frac{1}{1 + e^{-z}}\n",
        "   $$\n",
        "   Esta probabilidad indica la confianza de que la clase sea 1.\n",
        "\n",
        "3. **Clasificación Binaria:**\n",
        "   Para realizar la clasificación:\n",
        "   - Si $ P(Y=1 | X) \\geq 0.5 $, se predice la clase como $ 1 $.\n",
        "   - Si $ P(Y=1 | X) < 0.5 $, se predice la clase como $ 0 $.\n",
        "\n",
        "---\n",
        "\n",
        "### **Usos de la Regresión Logística**\n",
        "\n",
        "1. **Clasificación Binaria:**\n",
        "   Se utiliza para problemas donde la salida es binaria, como:\n",
        "   - Diagnóstico médico: ¿Tiene la enfermedad o no?\n",
        "   - Análisis de crédito: ¿Es un cliente riesgoso o no?\n",
        "   - Marketing: ¿Un usuario hará clic en un anuncio o no?\n",
        "\n",
        "2. **Probabilidades Interpretablas:**\n",
        "   La salida del modelo no solo es una clase, sino también una probabilidad que indica el grado de confianza en la predicción.\n",
        "\n",
        "3. **Modelado Lineal:**\n",
        "   Aunque utiliza una transformación no lineal (sigmoide), la relación entre las características y la probabilidad de la clase sigue siendo lineal.\n",
        "\n",
        "---\n",
        "\n",
        "### **Curva Logística**\n",
        "La curva logística muestra cómo la probabilidad varía en función de la combinación lineal de las características. Es una forma sigmoide que se aproxima a 0 para valores pequeños de $ z $ y a 1 para valores grandes.\n",
        "\n",
        "#### Gráfico de la Curva Logística"
      ],
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      },
      "id": "2w9t36VnJ4Pk"
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Valores de entrada (z)\n",
        "z = np.linspace(-10, 10, 100)\n",
        "\n",
        "# Función logística\n",
        "sigmoid = 1 / (1 + np.exp(-z))\n",
        "\n",
        "# Gráfico de la curva logística\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(z, sigmoid, label=r'$P(Y=1|X) = \\frac{1}{1 + e^{-z}}$', color='blue')\n",
        "plt.axhline(0.5, color='red', linestyle='--', label='P=0.5 (Umbral)')\n",
        "plt.title('Curva Logística')\n",
        "plt.xlabel('z (Combinación lineal de características)')\n",
        "plt.ylabel('Probabilidad')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "dXrnHdJUKlZi",
        "outputId": "05bdb1fc-a1ce-475b-fbcb-c07b8ddf0114"
      },
      "id": "dXrnHdJUKlZi",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aquí tienes la representación de la **curva logística**. La gráfica muestra cómo la probabilidad estimada por el modelo se ajusta en función de los valores de $ z $. Esta curva es el corazón de la regresión logística, permitiendo transformar una combinación lineal de variables en una probabilidad que se utiliza para la clasificación binaria."
      ],
      "metadata": {
        "id": "2AfG9geoKnne"
      },
      "id": "2AfG9geoKnne"
    },
    {
      "cell_type": "markdown",
      "id": "78b1336b",
      "metadata": {
        "id": "78b1336b"
      },
      "source": [
        "\n",
        "---\n",
        "\n",
        "### **1. Configurar PostgreSQL en Google Colab**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b2310c9b",
      "metadata": {
        "id": "b2310c9b"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "# Instalar PostgreSQL en Google Colab\n",
        "!apt update\n",
        "!apt install -y postgresql postgresql-contrib"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Iniciar el servicio PostgreSQL\n",
        "!service postgresql start\n",
        "\n",
        "# Crear usuario y base de datos\n",
        "!sudo -u postgres psql -c \"CREATE USER colab_user WITH PASSWORD 'password';\"\n",
        "!sudo -u postgres psql -c \"CREATE DATABASE colab_db;\"\n",
        "!sudo -u postgres psql -c \"GRANT ALL PRIVILEGES ON DATABASE colab_db TO colab_user;\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S-uI0y6t6bO4",
        "outputId": "ff6b7729-9eba-423b-93ba-4d407c8dcd7e"
      },
      "id": "S-uI0y6t6bO4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " * Starting PostgreSQL 14 database server\n",
            "   ...done.\n",
            "CREATE ROLE\n",
            "CREATE DATABASE\n",
            "GRANT\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "44e8d1e4",
      "metadata": {
        "id": "44e8d1e4"
      },
      "source": [
        "---\n",
        "\n",
        "### **2. Entrenar el Modelo en Python**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El dataset GermanCreditData incluye diferentes características, y seleccionaremos las que son más relevantes para el análisis. Vamos a usar un subconjunto de variables para evitar la redundancia y asegurarnos de que sean numéricas o correctamente codificadas:"
      ],
      "metadata": {
        "id": "X3yn7Jgr-Edn"
      },
      "id": "X3yn7Jgr-Edn"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b0da7585",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "b0da7585",
        "outputId": "ddd2f6c9-c269-4428-9005-0b966e8d53af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0      acc_status  acc_duration                credit_history  \\\n",
              "0         600     dm_0_to_200             7            credit_paid_so_far   \n",
              "1          80          no_acc            24            credit_paid_so_far   \n",
              "2         158     dm_0_to_200            24            credit_paid_so_far   \n",
              "3         423          no_acc            21  delay_in_past_credit_payment   \n",
              "4         747  dm_less_than_0            12            credit_paid_so_far   \n",
              "\n",
              "                  purpose  credit_amount            savings_acc  \\\n",
              "0             radio_or_tv           2329            dm_less_100   \n",
              "1             radio_or_tv           5943  unknown_or_no_savings   \n",
              "2                 car_new           3512          dm_100_to_500   \n",
              "3  furniture_or_equipment           1591          dm_100_to_500   \n",
              "4                 car_new           1274            dm_less_100   \n",
              "\n",
              "  employment_time  installment_rate  gender  ...      property  age  \\\n",
              "0        less_one                 1  female  ...   real_estate   45   \n",
              "1        less_one                 1  female  ...  car_or_other   44   \n",
              "2   four_to_seven                 2    male  ...  car_or_other   38   \n",
              "3   four_to_seven                 4    male  ...   real_estate   34   \n",
              "4        less_one                 3  female  ...   real_estate   37   \n",
              "\n",
              "  installment_plans  housing num_credits_at_bank  \\\n",
              "0              none      own                   1   \n",
              "1              none      own                   2   \n",
              "2              bank      own                   2   \n",
              "3              none      own                   2   \n",
              "4              none      own                   1   \n",
              "\n",
              "                                 job  num_people_maintenance phone  foreigner  \\\n",
              "0       skilled_employee_or_official                       1    no        yes   \n",
              "1       skilled_employee_or_official                       1   yes        yes   \n",
              "2       skilled_employee_or_official                       1   yes        yes   \n",
              "3  self_or_highly_qualified_employee                       1    no        yes   \n",
              "4                 unskilled_resident                       1    no        yes   \n",
              "\n",
              "  target  \n",
              "0      1  \n",
              "1      2  \n",
              "2      1  \n",
              "3      1  \n",
              "4      2  \n",
              "\n",
              "[5 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.datasets import fetch_openml\n",
        "\n",
        "# Cargar el dataset German Credit Risk\n",
        "data = fetch_openml('GermanCreditData', version=1, as_frame=True)\n",
        "df = data.frame\n",
        "y = (data.target == 2).astype(int)  # Convertir a 2 (Bad) y 1 (Good)\n",
        "\n",
        "df.head()"
      ]
    },
    {
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        "y.value_counts()"
      ],
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        "colab": {
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          "height": 178
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        "id": "KCfcPrA_BRTi",
        "outputId": "f36fa573-84d5-4839-c278-faf746a26dc4"
      },
      "id": "KCfcPrA_BRTi",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "target\n",
              "0    700\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.housing.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
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        "id": "R_9sqgCyCt2e",
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      "id": "R_9sqgCyCt2e",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "housing\n",
              "own         713\n",
              "rent        179\n",
              "for_free    108\n",
              "Name: count, dtype: int64"
            ],
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              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
          "execution_count": 5
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "# Seleccionar variables relevantes y codificar variables categóricas\n",
        "X = pd.get_dummies(df[['acc_duration', 'credit_amount', 'age', 'housing']], drop_first=True)\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HNwN4kto6vOf",
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      "id": "HNwN4kto6vOf",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   acc_duration  credit_amount  age  housing_own  housing_rent\n",
              "0             7           2329   45         True         False\n",
              "1            24           5943   44         True         False\n",
              "2            24           3512   38         True         False\n",
              "3            21           1591   34         True         False\n",
              "4            12           1274   37         True         False"
            ],
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              "      <th>acc_duration</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>age</th>\n",
              "      <th>housing_own</th>\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
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              "        document.querySelector('#df-30160b4a-e2c0-420a-88fc-0d55d48064ed button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-30160b4a-e2c0-420a-88fc-0d55d48064ed');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"acc_duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 4,\n        \"max\": 72,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          28,\n          14,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"credit_amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2822,\n        \"min\": 250,\n        \"max\": 18424,\n        \"num_unique_values\": 921,\n        \"samples\": [\n          882,\n          4439,\n          1288\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 19,\n        \"max\": 75,\n        \"num_unique_values\": 53,\n        \"samples\": [\n          27,\n          53,\n          67\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_own\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_rent\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Entrenar el modelo de regresión logística\n",
        "model = LogisticRegression(max_iter=2000, random_state=42).fit(X, y)\n",
        "\n",
        "# Obtener coeficientes y constante\n",
        "coefficients = model.coef_[0]\n",
        "intercept = model.intercept_[0]\n",
        "\n",
        "print(\"Coeficientes:\", coefficients)\n",
        "print(\"Intercepto:\", intercept)\n",
        "\n",
        "# Asignar coeficientes a variables específicas\n",
        "coef_duration = coefficients[0]\n",
        "coef_amount = coefficients[1]\n",
        "coef_age = coefficients[2]\n",
        "coef_housing_owned = coefficients[3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnQ94rozB6er",
        "outputId": "0e2dd871-fb06-4c24-c0c5-66f39242b642"
      },
      "id": "jnQ94rozB6er",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coeficientes: [ 3.36497943e-02  1.92540979e-05 -1.85319685e-02 -5.40539409e-01\n",
            "  2.87007840e-02]\n",
            "Intercepto: -0.6318592395276161\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En este caso:\n",
        "\n",
        "- `acc_duration`, `credit_amount`, y `age` son variables numéricas seleccionadas directamente.\n",
        "- `housing` se convierte en una variable *dummy*, codificando own como 1 y otras categorías como 0."
      ],
      "metadata": {
        "id": "5gT2mtMK-bc7"
      },
      "id": "5gT2mtMK-bc7"
    },
    {
      "cell_type": "markdown",
      "id": "0dbe9c0f",
      "metadata": {
        "id": "0dbe9c0f"
      },
      "source": [
        "---\n",
        "\n",
        "### **3. Crear el Procedimiento Almacenado en PostgreSQL**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0f26cc35",
      "metadata": {
        "id": "0f26cc35"
      },
      "outputs": [],
      "source": [
        "import psycopg2\n",
        "\n",
        "# Conectar a la base de datos PostgreSQL\n",
        "conn = psycopg2.connect(\n",
        "    dbname=\"colab_db\",\n",
        "    user=\"colab_user\",\n",
        "    password=\"password\",\n",
        "    host=\"localhost\"\n",
        ")\n",
        "cur = conn.cursor()\n",
        "\n",
        "# Crear el procedimiento almacenado con los coeficientes entrenados\n",
        "cur.execute(f\"\"\"\n",
        "CREATE OR REPLACE FUNCTION logistic_classifier(features DOUBLE PRECISION[])\n",
        "RETURNS TABLE(probability DOUBLE PRECISION, prediction INT)\n",
        "LANGUAGE plpgsql\n",
        "AS $$\n",
        "DECLARE\n",
        "\t  coef_duration DOUBLE PRECISION := {coef_duration};\n",
        "    coef_amount DOUBLE PRECISION := {coef_amount};\n",
        "    coef_age DOUBLE PRECISION := {coef_age};\n",
        "    coef_housing_owned DOUBLE PRECISION := {coef_housing_owned};\n",
        "    intercept DOUBLE PRECISION := {intercept};\n",
        "    prob DOUBLE PRECISION;\n",
        "    prediction INT;\n",
        "BEGIN\n",
        "    -- Calcular la probabilidad usando la regresión logística\n",
        "    prob := 1 / (1 + EXP(-(coef_duration * features[1] +\n",
        "                           coef_amount * features[2] +\n",
        "                           coef_age * features[3] +\n",
        "                           coef_housing_owned * features[4] +\n",
        "                           intercept)));\n",
        "\n",
        "    -- Asignar predicción binaria\n",
        "    IF prob >= 0.5 THEN\n",
        "        prediction := 1;\n",
        "    ELSE\n",
        "        prediction := 0;\n",
        "    END IF;\n",
        "\n",
        "    -- Devolver resultados como un conjunto\n",
        "    RETURN QUERY SELECT prob, prediction;\n",
        "END;\n",
        "$$;\n",
        "\"\"\")\n",
        "conn.commit()\n",
        "cur.close()\n",
        "conn.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a98020d1",
      "metadata": {
        "id": "a98020d1"
      },
      "source": [
        "---\n",
        "\n",
        "### **4. Invocar el Procedimiento desde Python**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e0db87d7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e0db87d7",
        "outputId": "f9e52faf-b7a7-40ed-9098-b7df6d50e16d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probabilidad: 0.1957031900004718, Predicción: 0\n"
          ]
        }
      ],
      "source": [
        "import psycopg2\n",
        "\n",
        "# Conectar a la base de datos PostgreSQL\n",
        "conn = psycopg2.connect(\n",
        "    dbname=\"colab_db\",\n",
        "    user=\"colab_user\",\n",
        "    password=\"password\",\n",
        "    host=\"localhost\"\n",
        ")\n",
        "cur = conn.cursor()\n",
        "\n",
        "# Ejecutar la función almacenada\n",
        "features = [12.0, 200.0, 35.0, 1.0]  # Ejemplo de datos de entrada\n",
        "cur.execute(\"SELECT * FROM logistic_classifier(%s)\", (features,))\n",
        "\n",
        "# Leer el resultado como ResultSet\n",
        "result = cur.fetchall()\n",
        "\n",
        "# Mostrar resultados\n",
        "for row in result:\n",
        "    print(f\"Probabilidad: {row[0]}, Predicción: {row[1]}\")\n",
        "\n",
        "# Cerrar conexión\n",
        "cur.close()\n",
        "conn.close()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9f9fbd56",
      "metadata": {
        "id": "9f9fbd56"
      },
      "source": [
        "## Notas:\n",
        "\n",
        "1. **Diferencia entre PROCEDURE y FUNCTION**: Un procedimiento (`PROCEDURE`) no puede devolver datos directamente, mientras que una función (`FUNCTION`) puede devolver valores o un conjunto de filas.\n",
        "2. **Formato del resultado**: La función usa `RETURNS TABLE` para especificar las columnas devueltas.\n",
        "3. **Pasar parámetros desde Python**: Los parámetros se pasan como una lista o tupla al ejecutar la función en PostgreSQL.\n",
        "4. **Extensibilidad:**: Puedes modificar el procedimiento para admitir más características o cambiar el umbral de clasificación.\n",
        "\n",
        "Con estos pasos, puedes entrenar un modelo de regresión logística en Python, implementar su lógica en PostgreSQL como un procedimiento almacenado y ejecutar predicciones desde Colab."
      ]
    }
  ],
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